Abstract
Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction.
Highlights
This paper proposes the Disaster Prediction Knowledge Graph (DPKG) that facilitates multi-source spatio-temporal data management and knowledge association
The main contributions of this paper are as follows: (1) This paper proposes a disaster prediction method which realizes dynamic data-driven disaster prediction based on the DPKG. (2) This paper proposes a fusion method for the dynamically changing spatiotemporal data and knowledge model
We demonstrate experiments and cases studies of the forest fire and geological landslide risk based on DPKG
Summary
Disaster is a general term for events that can have a destructive impact on humans and the environments on which they depend. It greatly increases the difficulty to resist natural disasters. Remote sensing technology can obtain a large amount of strongly dynamic information in large observation ranges with high speed in real-time [1]. It can identify the fine features of ground objects, and extract information such as buildings, roads, and cultivated land by combining remote sensing technology and artificial intelligence technology. It plays an important role in the fields of disaster monitoring, early warning, and emergency decision-making. The critical problem is the fusion of different data types, different data structures, and different types of expert knowledge when the remote sensing information extraction for the multi-scale, multi-temporal, multi-type, multi-precision remote sensing
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